This invention relates to neural networks and in particular to components therefor.
Neural networks are parallel distributed systems for processing information in a non-algorithmic way and are basically pattern recognition systems. Typical applications are for error correcting sub-systems in optical communications links, for associated memories in computing systems and for maintaining and controlling production lines where precise analytical procedures are not feasible and the control system has to be trained using expert knowledge. Optics is regarded as a promising technology for neural networks because of the ability to provide, economically, massively parallel interconnections. Common features uniting many neural network architectures are the multiplication of an input vector by a matrix of weights, followed by the application of a non-linear threshold to the components of the product vector. For those neural networks which can be represented mathematically by a matrix-vector multiplication, a conventional arrangement for the optical implementation of such a neural network is shown in FIG. 1 of the accompanying drawings. An input vector S=[S.sub.1 S.sub.2 . . . S.sub.N ].sup.T is realised as a column of stabilised light sources each of which illuminates uniformally a single row of pixels in a mask (matrix) M=[M.sub.ij ]. Optical routing devices (lenses or holograms) ensure that the light passing through the jth column of the mask is collected by the photodetector D.sub.j. The output vector is the row D=[D.sub.1 D.sub.2 . . . D.sub.N ]. The intensity I.sub.j of the light falling on the jth photodetector is ##EQU1## For example, in the Hopfield model [J. J. Hopfield, Proc. Nat. Acad. Sci. U.S.A. 79, 2554-58 (1982)] S.sub.i takes binary values, M.sub.ij may take analogue values in the range 0 to 1 and the threshold is "hard". I.sub.o is the intensity when only one source and one pixel are fully on. In the simplest version, a feedback arrangement is required which switches the source S.sub.j according to the conditions. EQU S.sub.j =1 (ON) when I.sub.j &gt;.theta..sub.j EQU S.sub.j =0 (OFF) when I.sub.j .ltoreq..theta..sub.j
where .theta..sub.j is a controllable threshold value. This is a "hard" threshold example.
When an arbitrary input vector is presented by forcing a pattern on the light sources for a short period, the system responds by finding and displaying the nearest matching pattern stored in the mask.
An optical neural network of this type is described, for example, in "Designs and devices for optical bidirectional associative memories" C. Guest et al. Applied Optics Vol 26, No. 23, 1 December 1987 p.5055-5060. A compact bidirectional associative memory implementation described therein employs a spatial light modulator device which comprises an array of single element detectors paired with optical modulators of a similar form. Light falling on a detector causes its associated modulator to become more transparent. The known spatial light modulator device shown in FIG. 2 of the accompanying drawings consists of alternating stripes of silicon photodetectors 1 and electro-optic modulators 2. The signal from each detector is amplified and thresholded by silicon drive circuitry 3 that then drives the associated modulator. Two such device arrays orientated orthogonally with a connection matrix (mask) 4 therebetween are used to implement an optical bidirectional associative memory (FIG. 3). The connection matrix may be a transparency. Light is introduced onto both faces of the resulting array. It is suggested in the aforementioned article that such spatial light modulator devices may be comprised by a hybrid system with silicon detectors and circuitry and PLZT modulators.
An object of the present invention is to provide a spatial light modulator by means of an alternative technology and hence to provide a so-called neural plane which is a basic element for neural networks and components thereof.